Based on this typical model of supervised VO, a number of works further extended this approach to improve the model performance.
To improve the generalization ability of supervised VO, [1]} incorporates curriculum learning (i.e. the model is trained by increasing the data complexity) and geometric loss constraints.
Knowledge distillation (i.e. a large model is compressed by teaching a smaller one) is applied into the supervised VO framework to greatly reduce the number of network parameters, making it more amenable for real-time operation on mobile devices [2]}.
Furthermore, Xue et al. [3]} introduced a memory module that stores global information, and a refining module that improves pose estimates with the preserved contextual information.
